Bio

My broad research interests include distributed systems and cloud computing – in particular, I am interested in the system-side problems associated with learning, deploying, and operationalizing machine learning models at scale.

Previously, I was a Research Fellow at Microsoft Research India and prior to that obtained my Masters (by Research) in Computer and Data Systems from the Indian Institute of Science (IISc).

Contact

  • Academic
    gandhis@stanford.edu
    University - Student Department: Computer Science Position: Graduate

Additional Info

Links

All Publications

  • ReCycle: Resilient Training of Large DNNs using Pipeline Adaptation ACM SIGOPS Symposium on Operating Systems Principles (SOSP) Gandhi, S., Zhao, M., Skiadopoulos, A., Kozyrakis, C. 2024

    View details for DOI 10.1145/3694715.3695960

  • Improving DNN Inference Throughput Using Practical, Per-Input Compute Adaptation ACM SIGOPS Symposium on Operating Systems Principles (SOSP) Iyer, A., Guan, M., Dai, Y., Pan, R., Gandhi, S., Netravali, R. 2024

    View details for DOI 10.1145/3694715.3695978

  • P3: Distributed Deep Graph Learning at Scale Gandhi, S., Iyer, A. P. USENIX Association. 2021 ; 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21) 551-568
  • An Interval-centric Model for Distributed Computing over Temporal Graphs Gandhi, S., Simmhan, Y., IEEE IEEE COMPUTER SOC. 2020: 1129-1140

    View details for DOI 10.1109/ICED48307.2020.00102

    View details for Web of Science ID 000584252700095

https://orcid.org/0000-0003-3689-9591